With the rapid development of the Internet, computer users all over the world are becoming increasingly familiar with writing English. Unfortunately, for some societies that possess significantly different cultures and writing styles, the ability to write in English is an ever-present barrier. This is not due to lack of knowledge, as research suggests that many non-English users have sufficient knowledge of English to easily discriminate between a sentence written in native-English and a sentence written in broken English. English is used an example, but the problem persists across other language boundaries.
Consider the plight of a Chinese user. Typically, when a Chinese user wants to write an English word/phrase in which he is unfamiliar with its spelling or usage, the user usually looks up the word/phrase in a Chinese-English dictionary. If the dictionary is an electronic dictionary, the user must input the Chinese word/phrase via some input mechanism. This process suffers three shortcomings. First, it is not convenient for a Chinese user to input a Chinese word/phrase. Second, forcing the user to enter a Chinese word/phrase interrupts the user's train of thought when writing in English. Third, as a non-native speaker of English, it is difficult for a Chinese user to select a suitable word from the dictionary.
Accordingly, there is a need for a machine-aided writing system that helps non-English users with spelling, grammar, and writing as a native-English user. As envisioned by the inventors, such a machine-aided writing system should act as a consultant that provides various kinds of help whenever necessary, and allows the users to control the writing. Such a system might provide spelling help to assist users with hard-to-spell words and simultaneously check the usage in a certain context. The machine-aided writing system might further provide some form of sentence help to let users refine the writing by providing perfect example sentences.
Several machine-aided approaches have been proposed. The approaches typically fall into two categories: (1) automatic translation, and (2) translation memory. Both work at the sentence level. The former attempts to automatically translate sentences entered by the user into sentences that are grammatically and stylistically correct. However, the quality of fully automatic machine translation in the current system is not completely satisfactory because a significant amount of manual editing is needed following such translation to ensure the high quality. The translation memory approach works like a case-based system in that, given a sentence, the system retrieves similar sentences from a translation example database. The user then translates the subject sentence by analogy.
While both approaches offer some advantages, there remains room to improve the user experience with computer-aided writing systems. More particularly, there is a need for a computer-aided writing system that allows non-English user to collaborate with the computer in a way that achieves the highest quality writing with less brute force effort.